CN105550804A - Machine tool product manufacturing system energy efficiency evaluation method based on gray fuzzy algorithm - Google Patents

Abstract

The invention provides a machine tool product manufacturing system energy efficiency evaluation method based on a gray fuzzy algorithm. First, a machine tool product manufacturing system energy efficiency comprehensive evaluation index system is established; and then, by adopting the ideal of combined weighting, the rough set theory and the analytic hierarchy process are combined to determine the weights of machine tool product manufacturing system energy efficiency indexes. Combination of qualitative analysis and quantitative analysis is realized, and determination of the weights of machine tool product manufacturing system energy efficiency indexes is more scientific and reasonable. On the basis, the invention provides an improved gray fuzzy energy efficiency analysis method by combining the grey relation theory and the triangle membership model, and the subjectivity and objectivity in the process of energy efficiency evaluation are well overcome. The method is scientific and reasonable. By calculating the energy efficiency, enterprises can improve the energy efficiency in a targeted manner and promote green manufacturing.

Description

Based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm

Technical field

The present invention relates to a kind of machine tool product manufacturing system efficiency evaluation method, relate to machine tool product comprehensive evaluation, green PRODUCTION TRAITS field.

Background technology

Manufacturing industry, as mainstay of the national economy industry, while creation tremendous economic wealth, also consumes a large amount of manufacturing recourses particularly energy, and causes having a strong impact on environment.Energy problem has become restriction economy and the factor directly perceived of social development, from the direction to energy utilization, energy-conservationly becomes the most important thing.The basic constituent element of typical Machine Manufacture system can be divided into production environment, production equipment, production object, operator's four parts.The energy that manufacturing system consumes in process of production can be divided into DIRECT ENERGY and indirect energy, and DIRECT ENERGY is the energy that the various processes manufactured a product consume, and indirect energy is the energy in order to the production environment needs in maintenance shop consume.

Strengthen enterprise's efficiency evaluation, raising system manufacturing system energy efficiency has become the manufacturing task of top priority.Efficiency is evaluated, and namely evaluates enterprise's efficiency utilization power in process of production, impels enterprise's improvement of production process and way to manage, thus has utilization to improve efficiency of energy utilization, economize energy.The evaluation of manufacturing system efficiency comprise manufacturing system energy ezpenditure state and energy consuming process assay and on this basis to the evaluation of energy efficiency.Improve the prerequisite of efficiency of energy utilization be understand energy consumption system itself with can situation, therefore research efficiency assessment method, the energy efficiency evaluation index system of Erecting and improving has realistic meaning.

Summary of the invention

The object of the present invention is to provide a kind of machine tool product manufacturing system efficiency evaluation method, this method avoid the impact of expert's subjective factor, it also avoid when under sample data not comprehensively situation simultaneously, the weight obtained, by the problem of substantial deviation reality, can provide foundation and guidance for machine tool product comprehensive evaluation.

In order to achieve the above object, machine tool product manufacturing system efficiency evaluation method of the present invention, comprises the steps:

Step one, set up machine tool product manufacturing system efficiency assessment indicator system, in efficiency assessment indicator system, all specific targets form factor of evaluation collection C;

The weight set W of the combined method agriculture products of step 2, using rough collection and analytical hierarchy process; Namely utilize rough set and analytical hierarchy process to obtain the index weights of objective, subjective two aspects respectively, carry out comprehensively, obtaining last index weights to both, obtain one group of final evaluation criterion weight

W＝μw _Ai+(1-μ)w _Bi

Wherein w _airefer to objective weight value, w _birefer to subjective weighted value, μ ∈ [0,1], the value of μ is determined as the case may be, close to 0, μ more represents that decision-making more tends to expertise, close to 1, μ more represents that decision-making more tends to objective data;

The method of step 3, application linear scale transform carries out nondimensionalization process to the original quantitative target data of machine tool product manufacturing system;

Step 4, application classification scoring carry out quantification process to the original qualitative index data of machine tool product manufacturing system;

Step 5, application triangle are subordinate to model determination single factor test fuzzy evaluation collection;

Step 6, calculate first class index Evaluations matrix according to Grey Incidence, and then obtain first class index evaluation result;

Step 7, the comprehensive alternate evaluation of Grey Incidence is utilized to go out multilayer index.

Concrete, described in step one, efficiency assessment indicator system comprises economic energy efficiency indexes, product energy efficiency indexes, energy efficiency of equipment index and flow of task energy efficiency indexes 4 first class index, the two-level index that described economic energy efficiency indexes comprises has: ten thousand yuan of product energy consumptions, ten thousand yuan of added value energy consumptions, the two-level index that described product energy efficiency indexes comprises has: unit product comprehensive energy consumption, unit product amount of energy saving, product energy level, the two-level index that described energy efficiency of equipment index comprises has: machine tool efficiency, energy transfer efficiency, energy processing conversion efficiency, the two-level index that described flow of task energy efficiency indexes comprises has: production technology efficiency, resources of production scheduling efficiency, these 10 two-level index form factor of evaluation collection C.

In step 3, if the raw value of a kth index is then to carry out nondimensionalization process through following formula, the data value C wherein after process _i(k) ∈ (0,1),

$C_i\left(k\right)=\frac{c_k^i-\min c_k^i}{\max c_k^i-\min c_k^i}$

And i=1,2 ... n, k=1,2 ... m, wherein m is decision index system quantity, and n is possibility quantity.

Step 4 is converted into quantitative target qualitative index, adopts classification scoring, gives a score value to every grade.

Step 5, from single index, determines the degree of membership evaluating element of set element; The FUZZY MAPPING of (V) from U to F:

$f:U\→F\left(V\right),\∀u_i\∈U,u_i|\→f\left(u_i\right)=\frac{r_{i,1}}{c_1}+\frac{r_{i,2}}{c_2}+...+\frac{r_{i,k}}{c_k}...+\frac{r_{i,m}}{c_m}$

In formula, r _i,krepresent u _ibelong to c _kdegree of membership.

Step 6 realizes the comprehensive evaluation of first class index according to Grey Incidence, and optimum index set is: $C*=\[\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\end{array}\],$ Iotave evaluation matrix is: $D=\left[\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\\ c_1^1& c_2^1& ...& c_m^1\\ ...& ...& ...& ...\\ c_1^n& c_2^n& ...& c_m^n\end{array}\right]$

In formula, m is decision index system quantity, and n is possibility quantity, for the optimal value of a kth index, it is the original value of a kth index in i-th scheme; The two poles of the earth lowest difference can be drawn:

The maximum difference in the two poles of the earth: ${\mathrm{TOW}}_{max}=\underset{i}{max}\underset{k}{max}\left|c_k^{*}-c_k^i\right|$

Grey incidence coefficient is:

$L_i^k=\frac{{\mathrm{TOW}}_{min}+{\mathrm{\ρTOW}}_{max}}{\left|c_k^{*}-c_k^i\right|+{\mathrm{\ρTOW}}_{max}},\mathrm{\ρ}\∈(0,1)$

Evaluations matrix is:

$R=\left[\begin{array}{cccc}{L}_1\left(1\right)& L_2\left(1\right)& ...& L_n\left(1\right)\\ L_1\left(2\right)& L_2\left(2\right)& ...& L_n\left(2\right)\\ ...& ...& ...& ...\\ L_1\left(m\right)& L_2\left(m\right)& ...& L_n\left(m\right)\end{array}\right]$

Last Grey Comprehensive Evaluation:

J＝W×R

In formula, W is weight matrix, and R is Evaluations matrix.

Step 7 realizes multistage Grey Comprehensive Evaluation: if index has y layer, then will carry out y level Grey Comprehensive Evaluation, c _kas a kth evaluation index, its single index evaluation collection wherein s is as index quantity; When index have two-layer and every layer have multiple index time, first single index fuzzy evaluation is carried out to second layer index, by second layer index, one-level Grey Comprehensive Evaluation is carried out to ground floor index again, carry out secondary Grey Comprehensive Evaluation by the one-level Grey Comprehensive Evaluation result of ground floor index to second layer index again, evaluation result is system evaluation result.

The invention has the beneficial effects as follows: first the present invention establishes machine tool product manufacturing system efficiency System of Comprehensive Evaluation, then the thought of combination weighting is adopted, rough set theory is combined the weight determining each index of machine tool product manufacturing system efficiency with analytical hierarchy process, achieve the combination of qualitative analysis and quantitative test, make the determination of machine tool product manufacturing system efficiency evaluation criterion weight more scientific, more reasonable.On this basis, synthetical grey relation theory of the present invention and triangle are subordinate to model, propose a kind of grey fuzzy energy efficiency analysis method for air of improvement, overcome the subjectivity in efficiency evaluation procedure and objectivity preferably.The method is scientific and reasonable, and enterprise, by calculating efficiency, can carry out efficiency improvement pointedly, advances green manufacturing.

Accompanying drawing explanation

Fig. 1 is efficiency evaluation rubric figure of the present invention.

Fig. 2 is System of Comprehensive Evaluation of the present invention.

Embodiment

The present invention mainly provides a kind of evaluation method for machine tool product manufacturing system efficiency comprehensive evaluation, as shown in Figure 1, the method mainly comprises following step: step one, set up machine tool product manufacturing system efficiency assessment indicator system and factor of evaluation collection C; The weight set W of step 2, using rough collection-AHM (analytical hierarchy process) combined method agriculture products; The method of step 3, application linear scale transform carries out nondimensionalization process to the original quantitative target data of machine tool product manufacturing system; Step 4, application classification scoring carry out quantification process to the original qualitative index data of machine tool product manufacturing system.Step 5, application triangle are subordinate to model determination single factor test fuzzy evaluation collection; Step 6, calculate one-level Evaluations matrix according to Grey Incidence, and then obtain one-level evaluation result; Step 7, the comprehensive alternate evaluation of Grey Incidence is utilized to go out multilayer index.

In step one: the purpose that the choosing of evaluation index must be noted that to evaluate, comprehensive, stability and feasibility principle, the determination of evaluation index will based on actual conditions, here economic efficiency, product efficiency, energy efficiency of equipment and flow of task efficiency 4 first class index are chosen and 10 two-level index set up energy sources balance index system, cover each index of machine tool product manufacturing system, gas producing formation, mechanical floor and task layer comprehensively, and traditional machine tool product manufacturing system efficiency appraisement system, have ignored production technology efficiency and resources of production scheduling efficiency.The hierarchical structure of whole index system of the present invention is as shown in Figure 2.This efficiency assessment indicator system comprises economic energy efficiency indexes B1, product energy efficiency indexes B2, energy efficiency of equipment index B3 and flow of task energy efficiency indexes B4 is totally 4 first class index, the two-level index that described economic energy efficiency indexes B1 comprises has: ten thousand yuan of product energy consumption C1, ten thousand yuan of added value energy consumption C2, the two-level index that described product energy efficiency indexes B2 comprises has: unit product comprehensive energy consumption C3, unit product amount of energy saving C4, the horizontal C5 of product energy, the two-level index that described energy efficiency of equipment index B3 comprises has: machine tool efficiency C6, energy transfer efficiency C7, energy processing conversion efficiency C8, the two-level index that described flow of task energy efficiency indexes B4 comprises has: production technology efficiency C9, resources of production scheduling efficiency C10, these 10 two-level index form factor of evaluation collection C.

Step 2 utilizes rough set theory processing advantage that is uncertain, imprecise data, can obtain comparatively objectively index weights information; Utilize AHM can make full use of the advantage of domain expertise on the other hand, obtaining expert to the objective Assessment of Important result of index, overcoming the deficiency that tradition stratum fractional analysis is higher to consistency check requirement when evaluating.

(1) based on the weighing computation method of rough set theory:

In decision table S=(U, C, D, V, f), decision attribute D (U/D={D ₁, D ₂... D _k) relative to conditional attribute collection C (U/C={C ₁, C ₂... C _m) conditional information entropy be:

$I\left(D\right|C)=\underset{i=1}{\overset{m}{\Σ}}\frac{{\left|C\right|}^2}{{\left|U\right|}^2}\underset{j=1}{\overset{k}{\Σ}}\frac{{\left|D_j\∩C_i\right|}^2}{{\left|C_i\right|}^2}(1-\frac{\left|D_j\∩C_i\right|}{\left|C_i\right|})$

Wherein U is object set, and subset C is conditional attribute collection, and D is decision kind set, C ∩ D=φ, and D ≠ φ, V are property value sets, and f represents an information function, and it represents the property value that in domain, each object is got in respective attributes.

In decision table S=(U, C, D, V, f), then the importance degree of conditional attribute (index) C is defined as

Sig(c)＝I(D|C-{c}-I(D|C))

In decision table S=(U, C, D, V, f), then the weight of conditional attribute (index) C is

$W_{Ai}\left(c\right)=\frac{Sig\left(c\right)+I\left(D\right|\left\{c\right\})}{\underset{a\∈C}{\Σ}\{Sig\left(a\right)+I(D\left|\right\{a\left\}\right\}}$

(2) based on the weighing computation method of AHM:

For calculating the relative importance between same layer element, set up judgment matrix A={a _ij, wherein a _ij=1/a _ji, a _ii=1.Wherein a _ijthe importance degree parameter obtained according to expertise, a _ij∈ { 1,3,5,7,9}.By A={a _ijto be converted into by formula and to estimate matrix

$\mathrm{\μ}=\left\{\begin{array}{cc}\frac{\mathrm{\β}k}{\mathrm{\β}k+1}& a_{ij}=k\\ \frac{1}{\mathrm{\β}k+1}& a_{ij}=\frac{1}{k}\\ 0.5& a_{ij}=1,i\≠j\\ 0& a_{ij}=1,i=j\end{array}\right.$

K be greater than 1 positive integer, get β=1

Calculate individual layer index weights, obtain the weighting subset of every layer of index relative to its upper strata index:

W＝[w ₁,w ₂...w ₁₀]，

$w_i=\frac{2}{n(n-1)}\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\μ}}_{ij},i=1,2,...,n,$

$\underset{i=1}{\overset{n}{\Σ}}{w}_i=1,0\≤w_i\≤1,$

n＝10。

Calculate the combining weights of bottom element

w _j＝w _i*w _ij

Wherein w _jrepresent the combining weights of jth item sub-goal for general objective, w _irepresent the combining weights of i-th sub-goal, w _ijrepresent that jth item sub-goal is to the weight of i-th sub-goal, wherein i-th sub-goal is positioned at the last layer of jth item sub-goal.Combining weights is mainly used to analyze the importance between each index, is not used in calculating below.

(3) evaluation index comprehensive weight computing function builds:

Rough set and AHM method is utilized to obtain the index weights of objective, subjective two aspects respectively, utilize rough set theory can process uncertain, coarse data, overcome the impact by expert's subjective factor, easily affect by the selection of sample data also, particularly under sample data not comprehensively situation, the weight obtained is by substantial deviation reality.And AHM method can make full use of the experience of expert, but very large by man's activity, can not sample weights be objectively responded.Therefore carry out comprehensively, obtaining last index weights to both, obtain one group of final evaluation criterion weight.

W＝μw _Ai+(1-μ)w _Bi

Wherein w _airefer to objective weight value, w _birefer to subjective weighted value, the value of μ is determined as the case may be, when decision-making tendency expertise, μ ∈ [0,0.5), and when decision-making tendency objective data, μ ∈ (0.5,1].Finally calculating the weight that obtains, is namely the weight in the last metrics evaluation that obtained by subjectivity and objectivity weight COMPREHENSIVE CALCULATING.

Step 3 achieves the nondimensionalization process of quantitative target, for quantitative target, due to measurement unit, the magnitude difference of each index, must carry out nondimensionalization process, to reduce the interference of enchancement factor to raw data index.If the raw value of a kth index is then to carry out nondimensionalization process through following formula, the data value C wherein after process _i(k) ∈ (0,1).

$C_i\left(k\right)=\frac{c_k^i-\min c_k^i}{\max c_k^i-\min c_k^i}$

And i=1,2 ... n, k=1,2 ... m, wherein m is decision index system quantity, and n is possibility quantity.

Step 4 is converted into quantitative target qualitative index, adopts classification scoring herein, gives a score value to every grade, if grade is " excellent, good, in, poor ", then score value is respectively " 4,3,2,1 ".

Step 5, from single index, determines the degree of membership evaluating element of set element.The FUZZY MAPPING of (V) from U to F:

$f:U\→F\left(V\right),\∀u_i\∈U,u_i|\→f\left(u_i\right)=\frac{r_{i,1}}{c_1}+\frac{r_{i,2}}{c_2}+...+\frac{r_{i,m}}{c_m}$

In formula, r _i,jrepresent u _ibelong to c _jdegree of membership.

Determine that the method for subordinate function has function rationalistic method, dualistic contrast compositor, fuzzy statistical method, threefold division and fuzzy distribution etc.Adopt triangle to be subordinate to model herein, subordinate function is as follows:

Be applicable to the index x that value is the smaller the better, its subordinate function model is as follows:

$u_1\left(x\right)=\left\{\begin{array}{cc}1& x\&le;a_1\\ (x-a_2)/(a_1-a_2)& a_1<x\&le;a_2\\ 0& x>a_2\end{array}\right.$

$u_2\left(x\right)=\left\{\begin{array}{cc}0& x\&le;a_{1}orx\&GreaterEqual;a_3\\ (x-a_1)/(a_2-a_1)& a_1<x<a_2\\ (x-a_3)(a_2-a_3)& a_2<x<a_3\end{array}\right.$

$u_3\left(x\right)=\left\{\begin{array}{cc}0& x\&le;a_2\\ (x-a_2)/(a_3-a_2)& a_2<x\&le;a_3\\ 1& x>a_3\end{array}\right.$

Be applicable to be worth the index x be the bigger the better, its subordinate function model is as follows:

$u_1\left(x\right)=\left\{\begin{array}{cc}0& x\&le;a_2\\ (x-a_2)/(a_1-a_2)& a_2<x\&le;a_1\\ 1& x>a_1\end{array}\right.$

$u_2\left(x\right)=\left\{\begin{array}{cc}0& x\&le;a_{3}orx\&GreaterEqual;a_1\\ (x-a_3)/(a_2-a_3)& a_3<x\&le;a_2\\ (x-a_3)(a_2-a_3)& a_2<x<a_1\end{array}\right.$

$u_3\left(x\right)=\left\{\begin{array}{cc}1& x\&le;a_3\\ (x-a_2)/(a_3-a_2)& a_3<x\&le;a_2\\ 0& x>a_2\end{array}\right.$

Being applicable to value should at the index x of certain fixed interval, and its subordinate function model is as follows:

$u_1\left(x\right)=\left\{\begin{array}{cc}1& a_{11}\&le;x\&le;a_{12}\\ (x-a_{11})(a_{11}-a_{21})& a_{21}\&le;x<a_{11}\\ (x-a_{22})(a_{12}-a_{22})& a_{12}<x<a_{22}\\ 0& x<a_{21}orx>a_{22}\end{array}\right.$

$u_2\left(x\right)=\left\{\begin{array}{cc}0& x\&le;a_{31}orx\&GreaterEqual;a_{32}or{a}_{11}\&le;x\&le;a_{12}\\ (x-a_{31})/(a_{21}-a_{31})& a_{31}<x<a_{11}\\ (x-a_{11})/(a_{21}-a_{11})& a_{21}\&le;x<a_{11}\\ (x-a_{12})/(a_{22}-a_{12})& a_{12}\&le;x<a_{22}\\ (x-a_{32})/(a_{22}-a_{32})& a_{22}\&le;x<a_{32}\end{array}\right.$

$u_3\left(x\right)=\left\{\begin{array}{cc}1& x\&le;a_{31}orx\&GreaterEqual;a_{32}\\ (x-a_{11})(a_{11}-a_{21})& a_{31}<x<a_{21}\\ (x-a_{22})(a_{12}-a_{22})& a_{22}<x<a_{32}\\ 0& a_{21}\&le;x\&le;a_{22}\end{array}\right.$

U ₁, u ₂, u ₃the degree of membership representing the good middle difference of single factor test, and u ₁, u ₂, u ₃meet following relation:

u ₁+u ₂+u ₃＝1

By f (u _i) single factor evaluation collection can be obtained:

R _i＝(r _i1,r _i2,…r _im)

According to single factor evaluation collection, draw evaluation result qualitatively.

Step 6 achieves the comprehensive evaluation of first class index according to Grey Incidence.Optimum index set is: $C*=\&lsqb;\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\end{array}\&rsqb;$ Iotave evaluation matrix is: $D=\left[\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\\ c_1^1& c_2^1& ...& c_m^1\\ ...& ...& ...& ...\\ c_1^n& c_2^n& ...& c_m^n\end{array}\right]$

In formula, m is decision index system quantity, and n is possibility quantity, for the optimal value of a kth index, it is the original value of a kth index in i-th scheme.The two poles of the earth lowest difference can be drawn:

The maximum difference in the two poles of the earth: ${\mathrm{TOW}}_{max}=\underset{i}{max}\underset{k}{max}\left|c_k^{*}-c_k^i\right|$

Grey incidence coefficient is:

$L_i^k=\frac{{\mathrm{TOW}}_{min}+{\mathrm{\&rho;TOW}}_{max}}{\left|c_k^{*}-c_k^i\right|+{\mathrm{\&rho;TOW}}_{max}},\mathrm{\&rho;}\&Element;(0,1)$

Evaluations matrix is:

$R=\left[\begin{array}{cccc}{L}_1\left(1\right)& L_2\left(1\right)& ...& L_n\left(1\right)\\ L_1\left(2\right)& L_2\left(2\right)& ...& L_n\left(2\right)\\ ...& ...& ...& ...\\ L_1\left(m\right)& L_2\left(m\right)& ...& L_n\left(m\right)\end{array}\right]$

Last Grey Comprehensive Evaluation:

J＝W×R

In formula, W is weight matrix, and R is Evaluations matrix.

Step 7 realizes multistage Grey Comprehensive Evaluation: if index has y layer, then will carry out y level Grey Comprehensive Evaluation, c _kas a kth evaluation index, its single index evaluation collection wherein s is as index quantity.As have when index two-layer and every layer have multiple index time, first single index fuzzy evaluation is carried out to second layer index, by second layer index, one-level Grey Comprehensive Evaluation is carried out to ground floor index again, carry out secondary Grey Comprehensive Evaluation by the one-level Grey Comprehensive Evaluation result of ground floor index to second layer index again, evaluation result is system evaluation result.

Below in conjunction with embodiment, the invention will be further described.

Choose first, Yi Liangjia Machine Manufacture enterprise and carry out comprehensive evaluation as evaluation object, the concrete numerical value of each index is as shown in table 1 below.

The numerical value of each index of table 1

The weight set W of agriculture products

Calculate the weight based on rough set theory:

The importance degree of each index is first calculated according to formula (2):

First class index:

$sig\left(B_1\right)=\frac{9}{34}sig\left(B_2\right)=\frac{7}{34}sig\left(B_3\right)=\frac{8}{34}sig\left(B_4\right)=\frac{10}{34}$

Two-level index:

sig(C ₁)＝0.618sig(C ₂)＝0.382sig(C ₃)＝0.323sig(C ₄)＝0.31sig(C ₅)＝0.367sig(C ₆)＝0.439

sig(C ₇)＝0.412sig(C ₈)＝0.149sig(C ₉)＝0.34sig(C ₁₀)＝0.66

Weight is calculated again according to formula (3):

w＝[0.260.210.2410.289]w ₁＝[0.630.37]w ₂＝[0.300.330.37]

w ₃＝[0.4520.410.138]w ₄＝[0.520.48]

Combining weights is calculated according to formula (2), (3):

$w\left(C_1\right)=\frac{3}{25}w\left(C_2\right)=\frac{3}{25}w\left(C_3\right)=\frac{1}{25}w\left(C_4\right)=\frac{2}{25}w\left(C_5\right)=\frac{3}{25}$

$w\left(C_6\right)=\frac{3}{25}w\left(C_7\right)=\frac{3}{25}w\left(C_8\right)=\frac{1}{25}w\left(C_9\right)=\frac{3}{25}w\left(C_{10}\right)=\frac{3}{25}$

Calculate the weight based on AHM:

Individual layer index weights:

w＝[0.2510.2630.2440.242]

w ₁＝[0.60.4]w ₂＝[0.2510.3720.377]w ₃＝[0.4910.3020.207]w ₄＝[0.3920.608]

Combining weights:

$\widetilde{w_1}=\&lsqb;\begin{array}{cc}0.128& 0.109\end{array}\&rsqb;\widetilde{w_2}=\&lsqb;\begin{array}{ccc}0.076& 0.083& 0.092\end{array}\&rsqb;\widetilde{w_3}=\&lsqb;\begin{array}{ccc}0.117& 0.113& 0.046\end{array}\&rsqb;\widetilde{w_4}=\&lsqb;\begin{array}{cc}0.107& 0.129\end{array}\&rsqb;$

Calculate final weight

The weight of subjective evaluation index is obtained respectively, calculation combination evaluation, according to formula W=μ w by rough set and AHM _ai+ (1-μ) w _bi, get μ=0.62, result deflection objective weight, comprehensive weight as Table 2,3.

The weight of each two-level index of table 2

The weight of each first class index of table 3

This shows that flow of task energy efficiency indexes is the key factor that machine tool product manufacturing system efficiency is evaluated.

First class index weight: W=[0.2570.2300.2420.271]

Two-level index weight: W ₁=[0.620.38] W ₂=[0.2810.3460.373]

W ₃＝[0.4670.3690.164]W ₄＝[0.4710.529]

The nondimensionalization process of quantitative target

Each index of first machine tool plant is drawn through nondimensionalization process:

C＝[0.360.130.770.850.660.50.750.50.251]

Each index of second machine tool plant is drawn through nondimensionalization process:

C＝[0.580.640.620.0360.3310.240.910.5]

Determine the score value of qualitative index

The product of first, second machine tool plant can hierarchical level be " good ", " in ", the score value of correspondence is 3,2.

Single index fuzzy evaluation

Determine the degree of membership of each index, by calculating:

R _i＝[0.50.60.60.70.60.70.40.50.40.5]

One-level Grey Comprehensive Evaluation

Determine optimum index set C ^*, and through the nondimensionalization process of quantitative target and the quantification process of qualitative index:

C ^*＝[0001111111]

Obtain according to formulae discovery: TOW _min=0TOW _max=0.964

Get ρ=0.5 to calculate:

L ₁(1)＝0.57L ₁(2)＝0.79L ₁(3)＝0.38L ₁(4)＝0.76L ₁(5)＝0.59

L ₁(6)＝0.49L ₁(7)＝0.66L ₁(8)＝0.49L ₁(9)＝0.39L ₁(10)＝1

L ₂(1)＝0.45L ₂(2)＝0.43L ₂(3)＝0.44L ₂(4)＝0.33L ₂(5)＝0.43L ₂(6)＝1L ₂(7)＝0.39L ₂(8)＝0.83

L ₂(9)＝1L ₂(10)＝0.49

$R=\left[\begin{array}{cc}0.57& 0.45\\ 0.79& 0.43\\ 0.38& 0.44\\ 0.76& 0.33\\ 0.59& 0.43\\ 0.49& 1\\ 0.66& 0.39\\ 0.49& 0.83\\ 0.39& 1\\ 1& 0.49\end{array}\right]$

Obtain according to formula J=W × R

$J_1=\&lsqb;\begin{array}{cc}0.629& 0.38\end{array}\&rsqb;\&times;\left[\begin{array}{cc}0.57& 0.45\\ 0.79& 0.43\end{array}\right]=\&lsqb;\begin{array}{cc}0.654& 0.442\end{array}\&rsqb;$

$J_2=\&lsqb;\begin{array}{ccc}0.281& 0.346& 0.373\end{array}\&rsqb;\&times;\left[\begin{array}{cc}0.38& 0.44\\ 0.76& 0.33\\ 0.59& 0.43\end{array}\right]=\&lsqb;\begin{array}{cc}0.59& 0.398\end{array}\&rsqb;$

$J_3=\&lsqb;\begin{array}{ccc}0.467& 0.369& 0.164\end{array}\&rsqb;\&times;\left[\begin{array}{cc}0.49& 1\\ 0.66& 0.39\\ 0.49& 0.83\end{array}\right]=\&lsqb;\begin{array}{cc}0.553& 0.747\end{array}\&rsqb;$

$J_4=\&lsqb;\begin{array}{cc}0.471& 0.529\end{array}\&rsqb;\&times;\left[\begin{array}{cc}0.39& 1\\ 1& 0.49\end{array}\right]=\&lsqb;\begin{array}{cc}0.713& 0.73\end{array}\&rsqb;$

Secondary Grey Comprehensive Evaluation

R=[J ₁j ₂j ₃j ₄] ^t, according to formula J=W × R,

J＝[0.630.58]

Can obtain according to computational analysis, enterprise's efficiency of first machine tool plant is 0.63, and enterprise's efficiency of second machine tool plant is 0.58.The efficiency comprehensive evaluation grade of first, second machine tool plant all belongs to middle rank, and in four secondary energy efficiency indexes, product energy efficiency indexes plays topmost effect.Energy efficiency of equipment index, task layer energy efficiency indexes gap compared with first machine tool plant of second machine tool plant are larger, second machine tool plant should in energy efficiency of equipment index, task layer energy efficiency indexes in addition emphasis improve, equipment needs change or improve, flow of task may need improving technique route or Job-Shop method.

The present invention is intended to for machine tool product manufacturing system efficiency assessment technique field provides a kind of method of efficiency comprehensive evaluation, the non-generic technician of this area can modify to described technical scheme on the basis of reading instructions of the present invention, or equivalent replacement and these amendments are carried out to wherein portion of techniques feature or replaces, do not make the essence of appropriate technical solution depart from spirit and scope that the present invention respectively implements technical scheme.

Claims (7)

1., based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, comprise the following steps:

Step one, set up machine tool product manufacturing system efficiency assessment indicator system, in efficiency assessment indicator system, all specific targets form factor of evaluation collection C;

The weight set W of the combined method agriculture products of step 2, using rough collection and analytical hierarchy process; Namely utilize rough set and analytical hierarchy process to obtain the index weights of objective, subjective two aspects respectively, carry out comprehensively, obtaining last index weights to both, obtain one group of final evaluation criterion weight

W＝μw _Ai+(1-μ)w _Bi

Wherein w _airefer to objective weight value, w _birefer to subjective weighted value, μ ∈ [0,1], the value of μ is determined as the case may be, close to 0, μ more represents that decision-making more tends to expertise, close to 1, μ more represents that decision-making more tends to objective data;

The method of step 3, application linear scale transform carries out nondimensionalization process to the original quantitative target data of machine tool product manufacturing system;

Step 4, application classification scoring carry out quantification process to the original qualitative index data of machine tool product manufacturing system;

Step 5, application triangle are subordinate to model determination single factor test fuzzy evaluation collection;

Step 6, calculate first class index Evaluations matrix according to Grey Incidence, and then obtain first class index evaluation result;

Step 7, Grey Incidence comprehensive evaluation is utilized to go out multilayer index.

2. as claimed in claim 1 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, described in step one, efficiency assessment indicator system comprises economic energy efficiency indexes, product energy efficiency indexes, energy efficiency of equipment index and flow of task energy efficiency indexes 4 first class index, the two-level index that described economic energy efficiency indexes comprises has: ten thousand yuan of product energy consumptions, ten thousand yuan of added value energy consumptions, the two-level index that described product energy efficiency indexes comprises has: unit product comprehensive energy consumption, unit product amount of energy saving, product energy level, the two-level index that described energy efficiency of equipment index comprises has: machine tool efficiency, energy transfer efficiency, energy processing conversion efficiency, the two-level index that described flow of task energy efficiency indexes comprises has: production technology efficiency, resources of production scheduling efficiency, these 10 two-level index form factor of evaluation collection C.

3. as claimed in claim 1 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, in step 3, if the raw value of a kth index is then to carry out nondimensionalization process through following formula, the data value C wherein after process _i(k) ∈ (0,1),

$C_i\left(k\right)=\frac{c_k^i-{\mathrm{minc}}_k^i}{{\mathrm{maxc}}_k^i-{\mathrm{minc}}_k^i}$

And i=1,2n, k=1,2m, wherein m is decision index system quantity, and n is possibility quantity.

4. as claimed in claim 1 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, step 4 is converted into quantitative target qualitative index, adopts classification scoring, gives a score value to every grade.

5. as claimed in claim 1 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, step 5, from single index, determines the degree of membership evaluating element of set element; The FUZZY MAPPING of (V) from U to F:

$f:U\&RightArrow;F\left(V\right),\&ForAll;u_i\&Element;U,u_i|\&RightArrow;f\left(u_i\right)=\frac{r_{i,1}}{c_1}+\frac{r_{i,2}}{c_2}+...+\frac{r_{i,k}}{c_k}...+\frac{r_{i,m}}{c_m}$

In formula, r _i,krepresent u _ibelong to c _kdegree of membership.

6., as claimed in claim 1 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, step 6 realizes the comprehensive evaluation of first class index according to Grey Incidence, and optimum index set is: $C*=\left[\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\end{array}\right],$ Iotave evaluation matrix is: $D=\left[\begin{array}{cccc}{c}_1^{*}& c_2^{*}& ...& c_m^{*}\\ c_1^1& c_2^1& ...& c_m^1\\ ...& ...& ...& ...\\ c_1^n& c_2^n& ...& c_m^n\end{array}\right]$

In formula, m is decision index system quantity, and n is possibility quantity, for the optimal value of a kth index, it is the original value of a kth index in i-th scheme; The two poles of the earth lowest difference can be drawn:

The maximum difference in the two poles of the earth: ${\mathrm{TOW}}_{max}=\underset{i}{max}\underset{k}{max}|c_k^{*}-c_k^i|$

Grey incidence coefficient is:

$\begin{array}{cc}{L}_i^k=\frac{{\mathrm{TOW}}_{min}+{\mathrm{\&rho;TOW}}_{\max }}{|c_k^{*}-c_k^i|+{\mathrm{\&rho;TOW}}_{max}},& \mathrm{\&rho;}\&Element;(0,1)\end{array}$

Evaluations matrix is:

$R=\left[\begin{array}{cccc}{L}_1\left(1\right)& L_2\left(1\right)& ...& L_n\left(1\right)\\ L_1\left(2\right)& L_2\left(2\right)& ...& L_n\left(2\right)\\ ...& ...& ...& ...\\ L_1\left(m\right)& L_2\left(m\right)& ...& L_n\left(m\right)\end{array}\right]$

Last Grey Comprehensive Evaluation:

J＝W×R

In formula, W is weight matrix, and R is Evaluations matrix.

7., as claimed in claim 6 based on the machine tool product manufacturing system efficiency evaluation method of grey fuzzy algorithm, it is characterized in that, step 7 realizes multistage Grey Comprehensive Evaluation: if index has y layer, then will carry out y level Grey Comprehensive Evaluation, c _kas a kth evaluation index, its single index evaluation collection wherein s is as index quantity; When index have two-layer and every layer have multiple index time, first single index fuzzy evaluation is carried out to second layer index, by second layer index, one-level Grey Comprehensive Evaluation is carried out to ground floor index again, carry out secondary Grey Comprehensive Evaluation by the one-level Grey Comprehensive Evaluation result of ground floor index to second layer index again, evaluation result is system evaluation result.